Knowledge Graphs

Name: Knowledge Graphs | Fundamentals, Techniques, and Applications
Brand: MIT Press
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Fundamentals, Techniques, and Applications

Mayank Kejriwal Craig A. Knoblock Pedro Szekely(Author)

MIT Press

Published on 30. March 2021

568 pages

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978-0-262-36188-0 (ISBN)

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Content

Intro
Title Page
Copyright
Table of Contents
List of Figures
List of Tables
Preface
I: Knowledge Graph Fundamentals
1. Introduction to Knowledge Graphs
1.1. Graphs
1.2. Representing Knowledge as Graphs
1.3. Examples of Knowledge Graphs
1.3.1. Example 1: Scientific Publications and Academics
1.3.2. Example 2: ECommerce, Products, and Companies
1.3.3. Example 3: Social Networks
1.3.4. Example 4: Geopolitical Events
1.4. How to Read This Text
1.5. Concluding Notes
1.6. Software and Resources
1.7. Bibliographic Notes
1.8. Exercises
2. Modeling and Representing Knowledge Graphs
2.1. Introduction
2.1.1. Resource Description Framework
2.1.2. RDF Serializations
2.2. RDF Schema
2.2.1. RDFS Classes
2.2.2. RDFS Properties
2.3. Property Centric Models
2.4. Wikidata Model
2.4.1. Wikidata Items
2.4.2. Wikidata Properties
2.5. The Semantic Web Layer Cake
2.6. Schema Heterogeneity and Semantic Labeling
2.7. Concluding Notes
2.8. Software and Resources
2.9. Bibliographic Notes
2.10. Exercises
II: Knowledge Graph Construction
3. Domain Discovery
3.1. Introduction
3.2. Focused Crawling
3.2.1. Main Design Elements of a Focused Crawler
3.2.2. Best First Crawlers
3.2.3. Semantic Crawlers
3.2.4. Learning Crawlers
3.2.5. Evaluation of Focused Crawling
3.3. Influential Systems and Methodologies
3.3.1. Context Focused Crawler
3.3.2. Domain Discovery Tool
3.4. Concluding Notes
3.5. Software and Resources
3.6. Bibliographic Notes
3.7. Exercises
4. Named Entity Recognition
4.1. Introduction
4.2. Why Is Information Extraction Hard?
4.3. Approaches for Named Entity Recognition
4.3.1. Supervised Approaches
4.3.2. Semisupervised and Unsupervised Approaches
4.4. Deep Learning for Named Entity Recognition
4.5. DomainSpecific Named Entity Recognition
4.6. Evaluating Information Extraction Quality
4.7. Concluding Notes
4.8. Software and Resources
4.9. Bibliographic Notes
4.10. Exercises
5. Web Information Extraction
5.1. Introduction
5.2. Wrapper Generation
5.2.1. Manually Constructed and Supervised Wrappers
5.2.2. Semisupervised Approaches
5.2.3. Unsupervised Approaches
5.2.4. Empirical Comparative Analyses
5.3. Beyond Wrappers: Information Extraction over Structured Data
5.4. Concluding Notes
5.5. Software and Resources
5.6. Bibliographic Notes
5.7. Exercises
6. Relation Extraction
6.1. Introduction
6.2. Ontologies and Programs
6.2.1. Automatic Content Extraction
6.2.2. Other Ontologies: A Brief Primer
6.3. Techniques for Relation Extraction
6.3.1. Supervised Relation Extraction
6.3.2. Evaluating Supervised Relation Extraction
6.3.3. Semisupervised Relation Extraction
6.3.4. Unsupervised Relation Extraction
6.4. Recent Research: Deep Learning for Relation Extraction
6.5. Beyond Relation Extraction: Event Extraction and Joint Information Extraction
6.6. Concluding Notes
6.7. Software and Resources
6.8. Bibliographic Notes
6.9. Exercises
7. Nontraditional Information Extraction
7.1. Introduction
7.2. Open Information Extraction
7.2.1. KnowItAll
7.2.2. TextRunner
7.2.3. Evaluating and Comparing Open Information Extraction Systems
7.3. Social Media Information Extraction
7.3.1. TWICAL
7.3.2. TwitIE
7.4. Other Kinds of Nontraditional Information Extraction
7.5. Concluding Notes
7.6. Software and Resources
7.7. Bibliographic Notes
7.8. Exercises
III: Knowledge Graph Completion
8. Instance Matching
8.1. Introduction
8.2. Formalism
8.3. Why Is Instance Matching Challenging?
8.4. Two Step Pipeline
8.4.1. Blocking
8.4.2. Similarity
8.5. Evaluating the TwoStep Pipeline
8.5.1. Evaluating Blocking
8.5.2. Evaluating Similarity
8.6. Postsimilarity Steps
8.6.1. Clustering and Transitive Closure
8.6.2. Entity Name System
8.7. Formalizing Instance Matching: Swoosh
8.8. A Note on Research Frontiers
8.9. Data Cleaning beyond Instance Matching
8.10. Concluding Notes
8.11. Software and Resources
8.12. Bibliographic Notes
8.13. Exercises
9. Statistical Relational Learning
9.1. Introduction
9.2. Modeling Dependencies
9.3. Statistical Relational Learning Frameworks
9.3.1. Markov Logic Networks
9.3.2. Probabilistic Soft Logic
9.4. Knowledge Graph Identification
9.4.1. Representing Uncertain Extractions
9.4.2. Representing Instance Matching Outputs
9.4.3. Enforcement of Ontological Constraints
9.4.4. Putting It Together: Probabilistic Distributions over Uncertain Knowledge Graphs
9.4.5. A Note on Experimental Performance
9.5. Other Applications
9.5.1. Collective Classification
9.5.2. Link Prediction
9.5.3. Social Network Modeling
9.6. Advanced Research: Data Programming
9.7. Concluding Notes
9.8. Software and Resources
9.9. Bibliographic Notes
9.10. Exercises
10. Representation Learning for Knowledge Graphs
10.1. Introduction
10.2. Embedding Architectures: A Primer
10.2.1. Continuous Bag of Words Model
10.2.2. Skip-Gram Model
10.3. Embeddings beyond Words
10.4. Knowledge Graph Embeddings
10.4.1. Energy Functions
10.5. Influential KGE Systems
10.5.1. Structured Embeddings
10.5.2. Neural Tensor Networks
10.5.3. Translational Embedding Models
10.5.4. TransE
10.5.5. Other Trans* Algorithms
10.6. Extrafactual Contexts
10.6.1. Entity Types
10.6.2. Textual Data
10.6.3. Beyond Text and Concepts: Other Information Sets
10.7. Applications
10.7.1. Link Prediction
10.7.2. Triple Classification
10.7.3. Entity Classification
10.7.4. Revisiting Instance Matching
10.7.5. Other Applications
10.8. Concluding Notes
10.9. Software and Resources
10.10. Bibliographic Notes
10.11. Exercises
IV: Accessing Knowledge Graphs
11. Reasoning and Retrieval
11.1. Introduction
11.2. Reasoning
11.2.1. Description Logics: A Brief Primer
11.2.2. Web Ontology Language
11.2.3. Sample Reasoning Framework: Protégé
11.3. Retrieval
11.3.1. Term Frequency and Weighting
11.4. Retrieval versus Reasoning
11.4.1. Evaluation
11.4.2. Sample Information Retrieval Framework: Lucene
11.5. Concluding Notes
11.6. Software and Resources
11.7. Bibliographic Notes
11.8. Exercises
12. Structured Querying
12.1. Introduction
12.2. SPARQL
12.2.1. Subqueries
12.3. Relational Processing of Queries over Knowledge Graphs
12.3.1. Triple (Vertical) Stores
12.3.2. Property Table Stores
12.3.3. Horizontal Stores
12.4. NoSQL
12.4.1. Key Value Stores
12.4.2. Graph Databases
12.4.3. NoSQL Databases with Extreme Scalability
12.5. Concluding Notes
12.6. Software and Resources
12.7. Bibliographic Notes
12.8. Exercises
13. Question Answering
13.1. Introduction
13.2. Question Answering as a Stand-Alone Application
13.2.1. Learning from Conversational Dialogue: KnowBot
13.2.2. Bidirectional Encoder Representations from Transformers
13.2.3. Necessity of Knowledge Graphs
13.3. Question Answering as Knowledge Graph Querying
13.3.1. Challenges and Solutions
13.3.2. Template-Based Solutions
13.3.3. Evaluation of SQA
13.4. Concluding Notes
13.5. Software and Resources
13.5.1. BERT and Language Model-Based Question Answering
13.5.2. HOBBIT
13.6. Bibliographic Notes
13.7. Exercises
V: Knowledge Graph Ecosystems
14. Linked Data
14.1. Introduction
14.1.1. Principle 1: Use Uniform Resource Identifiers for Naming Things
14.1.2. Principle 2: Use HTTP Uniform Resource Identifiers
14.1.3. Principle 3: Provide Useful Information on Lookup Using Standards
14.1.4. Principle 4: Link New Data to Existing Data
14.2. Impact and Adoption of Linked Data Principles
14.2.1. Overall Impact
14.3. Important Knowledge Graphs in Linked Open Data
14.3.1. DBpedia
14.3.2. GeoNames
14.3.3. YAGO
14.3.4. Wikidata
14.3.5. Upper Mapping and Binding Exchange Layer
14.3.6. Friend of a Friend
14.3.7. Other Examples
14.4. Concluding Notes
14.5. Software and Resources
14.6. Bibliographic Notes
14.7. Exercises
15. Enterprise and Government
15.1. Introduction
15.2. Enterprise
15.2.1. Knowledge Vault
15.2.2. Social Media and Open Graph Protocol
15.2.3. Schema.org
15.3. Governments and Nonprofits
15.3.1. Open Government Data
15.3.2. BBC
15.3.3. OpenStreetMap
15.4. Where Is the Future Headed?
15.5. Concluding Notes
15.6. Software and Resources
15.7. Bibliographic Notes
15.8. Exercises
16. Knowledge Graphs and Ontologies in Science
16.1. Introduction
16.2. Biology
16.2.1. Gene Ontology
16.3. Chemistry
16.3.1. Chemical Entities of Biological Interest
16.3.2. PubChem
16.4. Earth, Environment, and Geosciences
16.4.1. Semantic Web for Earth and Environmental Terminology
16.4.2. The GEON Portal and OpenTopography
16.4.3. Environment Ontology
16.5. Concluding Notes
16.6. Software and Resources
16.7. Bibliographic Notes
16.8. Exercises
17. Knowledge Graphs for Domain-Specific Social Impact
17.1. Introduction
17.2. Domain Specific Insight Graphs
17.2.1. Domain Setup
17.2.2. Domain Exploration
17.3. Alternative System: DeepDive
17.4. Applications and Use-Cases
17.4.1. Investigative Domains
17.4.2. Crisis Informatics
17.4.3. COVID19 and Medical Informatics
17.5. Concluding Notes
17.6. Software and Resources
17.7. Bibliographic Notes
17.8. Exercises
Bibliography
Index

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Knowledge Graphs

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